{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1b85632-e392-4b43-bd85-65a22654eca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.13.2'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn\n",
    "seaborn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4eca37-7242-4ba2-927d-4886568f9842",
   "metadata": {},
   "source": [
    "【1】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8782050-51da-4aec-b8e3-8718fe8ff06f",
   "metadata": {},
   "source": [
    "seaborn数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d518887d-9bc3-41df-a740-350bec573cf1",
   "metadata": {},
   "source": [
    "1 seaborn输入数据格式"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "966abaed-a2ab-4206-817c-d6e2d50dd427",
   "metadata": {},
   "source": [
    "seaborn支持多种python数据集格式，如python列表(list)、字典(dict)、NumPy\n",
    "(numpy.ndarray)、pandas (pandas.Series、pandas.DataFrame)等等，熟\r\n",
    "这些数据结构可让seaborn使用起来得心应手。\r\n",
    "也就是说，使用seaborn时，数据处理步骤需要先将xlsx、tsv、txt等格原始\r\n",
    "数据简单转换为python数据格式"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e4b5cd-d664-44f3-b6cf-b324c77513c2",
   "metadata": {},
   "source": [
    "2 使用的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5878a3-7e83-4676-b169-459160edc0d0",
   "metadata": {},
   "source": [
    "2.1 tips数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39516f81-f0e6-4f34-934f-f7cf3e36a307",
   "metadata": {},
   "source": [
    "小费（tips）收入记录数据集（244行，7列），源自一位服务员在一家餐厅工作\n",
    "数月，记录他收到的每笔小费的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4abbd81c-3f66-47ab-a4ed-42137635c36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>消费金额 ($)</th>\n",
       "      <th>小费金额 ($)</th>\n",
       "      <th>客人性别</th>\n",
       "      <th>是否吸烟</th>\n",
       "      <th>周几</th>\n",
       "      <th>就餐时间</th>\n",
       "      <th>一起就餐人数 (个)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     消费金额 ($)  小费金额 ($)    客人性别 是否吸烟    周几    就餐时间  一起就餐人数 (个)\n",
       "0       16.99      1.01  Female   No   Sun  Dinner           2\n",
       "1       10.34      1.66    Male   No   Sun  Dinner           3\n",
       "2       21.01      3.50    Male   No   Sun  Dinner           3\n",
       "3       23.68      3.31    Male   No   Sun  Dinner           2\n",
       "4       24.59      3.61  Female   No   Sun  Dinner           4\n",
       "..        ...       ...     ...  ...   ...     ...         ...\n",
       "239     29.03      5.92    Male   No   Sat  Dinner           3\n",
       "240     27.18      2.00  Female  Yes   Sat  Dinner           2\n",
       "241     22.67      2.00    Male  Yes   Sat  Dinner           2\n",
       "242     17.82      1.75    Male   No   Sat  Dinner           2\n",
       "243     18.78      3.00  Female   No  Thur  Dinner           2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "tips = pd.read_csv('./seaborn数据集/tips.csv')\n",
    "tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97bbf6bf-8d2c-47da-98d5-55fa902ed310",
   "metadata": {},
   "source": [
    "2.2 penguins数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ea2224-291d-411e-8503-915e2b56c22b",
   "metadata": {},
   "source": [
    "企鹅（penguins）尺寸特征数据集（344行，7列），由Dr. Kristen Gorman收\n",
    "集自帕尔默南极科学考察站附近的帕尔默群岛上的成年觅食企鹅预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd405cd8-081c-4d05-8242-d15df61fe776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>企鹅的种类</th>\n",
       "      <th>岛屿</th>\n",
       "      <th>喙长 (毫米)</th>\n",
       "      <th>喙深 (毫米)</th>\n",
       "      <th>鳍长 (毫米)</th>\n",
       "      <th>体重</th>\n",
       "      <th>性别</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222.0</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>FEMALE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>Biscoe</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>MALE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      企鹅的种类         岛屿  喙长 (毫米)  喙深 (毫米)  鳍长 (毫米)      体重      性别\n",
       "0    Adelie  Torgersen     39.1     18.7    181.0  3750.0    MALE\n",
       "1    Adelie  Torgersen     39.5     17.4    186.0  3800.0  FEMALE\n",
       "2    Adelie  Torgersen     40.3     18.0    195.0  3250.0  FEMALE\n",
       "3    Adelie  Torgersen      NaN      NaN      NaN     NaN     NaN\n",
       "4    Adelie  Torgersen     36.7     19.3    193.0  3450.0  FEMALE\n",
       "..      ...        ...      ...      ...      ...     ...     ...\n",
       "339  Gentoo     Biscoe      NaN      NaN      NaN     NaN     NaN\n",
       "340  Gentoo     Biscoe     46.8     14.3    215.0  4850.0  FEMALE\n",
       "341  Gentoo     Biscoe     50.4     15.7    222.0  5750.0    MALE\n",
       "342  Gentoo     Biscoe     45.2     14.8    212.0  5200.0  FEMALE\n",
       "343  Gentoo     Biscoe     49.9     16.1    213.0  5400.0    MALE\n",
       "\n",
       "[344 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "penguins = pd.read_csv('./seaborn数据集/penguins.csv')\n",
    "penguins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60938bdd-d385-46bc-986d-d5ffa96150de",
   "metadata": {},
   "source": [
    "2.3 dowjones数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e151bb0f-169d-43dd-aff1-fe24c6a9acbc",
   "metadata": {},
   "source": [
    "美国道琼斯工业股票价格指数（Dow-Jones Industrial Stock Price Index forUnited States，dowjones）数据集（649行，2列），记录1914年12月到1968年12月之间每月1号的美国道琼斯工业股票价格。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5924c0f-5479-41fd-9d4c-902d02248a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>股票价格 ($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1914-12-01</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1915-01-01</td>\n",
       "      <td>56.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1915-02-01</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1915-03-01</td>\n",
       "      <td>58.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1915-04-01</td>\n",
       "      <td>66.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1968-08-01</td>\n",
       "      <td>883.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1968-09-01</td>\n",
       "      <td>922.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>1968-10-01</td>\n",
       "      <td>955.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1968-11-01</td>\n",
       "      <td>964.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1968-12-01</td>\n",
       "      <td>965.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            日期  股票价格 ($)\n",
       "0   1914-12-01     55.00\n",
       "1   1915-01-01     56.55\n",
       "2   1915-02-01     56.00\n",
       "3   1915-03-01     58.30\n",
       "4   1915-04-01     66.45\n",
       "..         ...       ...\n",
       "644 1968-08-01    883.72\n",
       "645 1968-09-01    922.80\n",
       "646 1968-10-01    955.47\n",
       "647 1968-11-01    964.12\n",
       "648 1968-12-01    965.39\n",
       "\n",
       "[649 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dowjones = pd.read_csv('./seaborn数据集/dowjones.csv')\n",
    "dowjones\n",
    "dowjones[\"日期\"] = pd.to_datetime(dowjones[\"日期\"])\n",
    "dowjones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e5b248-38f0-449d-b34b-d059f3d61d1a",
   "metadata": {},
   "source": [
    "2.4 fmri数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84ab728d-66f9-408b-9180-8fad8d6a0fd3",
   "metadata": {},
   "source": [
    "大脑成像技术或功能磁共振成像技术（Functional magnetic resonance\n",
    "imaging，frmi）数据集（1065行，5列），收录不同“测量对象”在某个“测量\r\n",
    "间”，受到某个“刺激事件”的刺激，“大脑区域”fMRI“信号”变化情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ad7fe40-134c-4030-bdf5-87c0127e7275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>测量对象</th>\n",
       "      <th>测量时间</th>\n",
       "      <th>刺激事件</th>\n",
       "      <th>大脑区域</th>\n",
       "      <th>fMRI信号</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s13</td>\n",
       "      <td>18</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.017552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s5</td>\n",
       "      <td>14</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.080883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s12</td>\n",
       "      <td>18</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.081033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s11</td>\n",
       "      <td>18</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.046134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s10</td>\n",
       "      <td>18</td>\n",
       "      <td>stim</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.037970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>s0</td>\n",
       "      <td>8</td>\n",
       "      <td>cue</td>\n",
       "      <td>frontal</td>\n",
       "      <td>0.018165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>s13</td>\n",
       "      <td>7</td>\n",
       "      <td>cue</td>\n",
       "      <td>frontal</td>\n",
       "      <td>-0.029130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>s12</td>\n",
       "      <td>7</td>\n",
       "      <td>cue</td>\n",
       "      <td>frontal</td>\n",
       "      <td>-0.004939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1062</th>\n",
       "      <td>s11</td>\n",
       "      <td>7</td>\n",
       "      <td>cue</td>\n",
       "      <td>frontal</td>\n",
       "      <td>-0.025367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>s0</td>\n",
       "      <td>0</td>\n",
       "      <td>cue</td>\n",
       "      <td>parietal</td>\n",
       "      <td>-0.006899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1064 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     测量对象  测量时间  刺激事件      大脑区域    fMRI信号\n",
       "0     s13    18  stim  parietal -0.017552\n",
       "1      s5    14  stim  parietal -0.080883\n",
       "2     s12    18  stim  parietal -0.081033\n",
       "3     s11    18  stim  parietal -0.046134\n",
       "4     s10    18  stim  parietal -0.037970\n",
       "...   ...   ...   ...       ...       ...\n",
       "1059   s0     8   cue   frontal  0.018165\n",
       "1060  s13     7   cue   frontal -0.029130\n",
       "1061  s12     7   cue   frontal -0.004939\n",
       "1062  s11     7   cue   frontal -0.025367\n",
       "1063   s0     0   cue  parietal -0.006899\n",
       "\n",
       "[1064 rows x 5 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "fmri= pd.read_csv('./seaborn数据集/fmri.csv')\n",
    "fmri"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfc0bd1-f87c-4e71-a49a-f1cbe0108a32",
   "metadata": {},
   "source": [
    "2.5 dots数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce44f152-0e69-4449-834b-704099b23039",
   "metadata": {},
   "source": [
    "dots数据集（848行，5列），源自恒河猴完成方向辨别任务的试验，这些试验\n",
    "中猴子通过选择两个选择目标之一完成任务，记录猴子对刺激的反应以及神经\r\n",
    "活动的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cb5d470-f097-4106-b609-a9d36f5ed640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>反应时机</th>\n",
       "      <th>选择目标</th>\n",
       "      <th>反应时间</th>\n",
       "      <th>一致性水平</th>\n",
       "      <th>神经元射频</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dots</td>\n",
       "      <td>T1</td>\n",
       "      <td>-80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.189967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dots</td>\n",
       "      <td>T1</td>\n",
       "      <td>-80</td>\n",
       "      <td>3.2</td>\n",
       "      <td>31.691726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dots</td>\n",
       "      <td>T1</td>\n",
       "      <td>-80</td>\n",
       "      <td>6.4</td>\n",
       "      <td>34.279840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dots</td>\n",
       "      <td>T1</td>\n",
       "      <td>-80</td>\n",
       "      <td>12.8</td>\n",
       "      <td>32.631874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dots</td>\n",
       "      <td>T1</td>\n",
       "      <td>-80</td>\n",
       "      <td>25.6</td>\n",
       "      <td>35.060487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>sacc</td>\n",
       "      <td>T2</td>\n",
       "      <td>300</td>\n",
       "      <td>3.2</td>\n",
       "      <td>33.281734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>844</th>\n",
       "      <td>sacc</td>\n",
       "      <td>T2</td>\n",
       "      <td>300</td>\n",
       "      <td>6.4</td>\n",
       "      <td>27.583979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>sacc</td>\n",
       "      <td>T2</td>\n",
       "      <td>300</td>\n",
       "      <td>12.8</td>\n",
       "      <td>28.511530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>sacc</td>\n",
       "      <td>T2</td>\n",
       "      <td>300</td>\n",
       "      <td>25.6</td>\n",
       "      <td>27.009804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>sacc</td>\n",
       "      <td>T2</td>\n",
       "      <td>300</td>\n",
       "      <td>51.2</td>\n",
       "      <td>30.959302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>848 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     反应时机 选择目标  反应时间  一致性水平      神经元射频\n",
       "0    dots   T1   -80    0.0  33.189967\n",
       "1    dots   T1   -80    3.2  31.691726\n",
       "2    dots   T1   -80    6.4  34.279840\n",
       "3    dots   T1   -80   12.8  32.631874\n",
       "4    dots   T1   -80   25.6  35.060487\n",
       "..    ...  ...   ...    ...        ...\n",
       "843  sacc   T2   300    3.2  33.281734\n",
       "844  sacc   T2   300    6.4  27.583979\n",
       "845  sacc   T2   300   12.8  28.511530\n",
       "846  sacc   T2   300   25.6  27.009804\n",
       "847  sacc   T2   300   51.2  30.959302\n",
       "\n",
       "[848 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dots= pd.read_csv('./seaborn数据集/dots.csv')\n",
    "dots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdadb8c1-c886-4b9d-95d2-5678ce8859e6",
   "metadata": {},
   "source": [
    "2.6 diamonds数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e995ce-0845-4793-b636-ab07bbbc3f27",
   "metadata": {},
   "source": [
    "钻石（diamonds）数据集（53940行，10列），记录钻石价格和其它属性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0ceed43d-409b-42a8-afb8-e15b461244d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>克拉重量</th>\n",
       "      <th>切工</th>\n",
       "      <th>颜色</th>\n",
       "      <th>净度</th>\n",
       "      <th>总深度百分比</th>\n",
       "      <th>桌面比例</th>\n",
       "      <th>价格 ($)</th>\n",
       "      <th>长度 (毫米)</th>\n",
       "      <th>宽度 (毫米)</th>\n",
       "      <th>深度 (毫米)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       克拉重量         切工 颜色   净度  总深度百分比  桌面比例  价格 ($)  长度 (毫米)  宽度 (毫米)  \\\n",
       "0      0.23      Ideal  E  SI2    61.5  55.0     326     3.95     3.98   \n",
       "1      0.21    Premium  E  SI1    59.8  61.0     326     3.89     3.84   \n",
       "2      0.23       Good  E  VS1    56.9  65.0     327     4.05     4.07   \n",
       "3      0.29    Premium  I  VS2    62.4  58.0     334     4.20     4.23   \n",
       "4      0.31       Good  J  SI2    63.3  58.0     335     4.34     4.35   \n",
       "...     ...        ... ..  ...     ...   ...     ...      ...      ...   \n",
       "53935  0.72      Ideal  D  SI1    60.8  57.0    2757     5.75     5.76   \n",
       "53936  0.72       Good  D  SI1    63.1  55.0    2757     5.69     5.75   \n",
       "53937  0.70  Very Good  D  SI1    62.8  60.0    2757     5.66     5.68   \n",
       "53938  0.86    Premium  H  SI2    61.0  58.0    2757     6.15     6.12   \n",
       "53939  0.75      Ideal  D  SI2    62.2  55.0    2757     5.83     5.87   \n",
       "\n",
       "       深度 (毫米)  \n",
       "0         2.43  \n",
       "1         2.31  \n",
       "2         2.31  \n",
       "3         2.63  \n",
       "4         2.75  \n",
       "...        ...  \n",
       "53935     3.50  \n",
       "53936     3.61  \n",
       "53937     3.56  \n",
       "53938     3.74  \n",
       "53939     3.64  \n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "diamonds = pd.read_csv('./seaborn数据集/diamonds.csv')\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f6a843-c9c6-48e5-b411-9eeb5491f8eb",
   "metadata": {},
   "source": [
    "【2】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "201e4be2-dbbd-4df5-8beb-b706b6136cff",
   "metadata": {},
   "source": [
    "seaborn使用简介"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef984259-9c2a-4864-b23c-a24f28a66fe7",
   "metadata": {},
   "source": [
    "1 快速上手"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207e3005-ab63-4580-a3b2-fb0954b2c691",
   "metadata": {},
   "source": [
    "seaborn通过各种函数可视化数据，以seaborn.relplot函数绘制相关关系图为\n",
    "例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0083324-910b-44d1-a3e3-1a21cd805c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1093x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一步：导入依赖包\n",
    "import seaborn as sns # 导入seaborn包并简写为sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 忽略警告\n",
    "warnings.filterwarnings(\"ignore\",\"is_categorical_dtype\")\n",
    "warnings.filterwarnings(\"ignore\",\"use_inf_as_na\")\n",
    "warnings.simplefilter('always',category=UserWarning)\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 第二布：设置绘图风格\n",
    "custom_set = {\n",
    "    \"font.family\":\"SimSun\",\n",
    "    \"axes.spines.left\":True,  # y坐标轴开启\n",
    "    \"axes.spines.bottom\":True,  # x坐标轴开启\n",
    "    \"axes.spines.top\":False, # 上方坐标轴关闭\n",
    "    \"axes.spines.right\":False,  # 右侧坐标轴关闭\n",
    "    \"axes.edgecolor\":\"gray\",   # 坐标轴颜色\n",
    "    \"axes.linewidth\":0.4,  # 坐标轴宽度\n",
    "    \"grid.linewidth\":0.3,   # 网格线宽度\n",
    "    \"grid.alpha\":1,      # 网格线透明度\n",
    "    \"legend.frameon\":True, # 图例边框开启\n",
    "    \"legend.framealpha\":0.5   # 图例边框透明度\n",
    "}\n",
    "\n",
    "sns.set_theme(\n",
    "    style='whitegrid',  # 设置图表风格\n",
    "    rc=custom_set,      # 坐标轴，图例，网格线等其他设置\n",
    "    font_scale=0.8,     # 文本字号\n",
    ")\n",
    "\n",
    "# 第三步：准备绘图数据\n",
    "tips = pd.read_csv('./seaborn数据集/tips.csv')  # 使用pandas读取数据\n",
    "\n",
    "# 第四步：seaborn绘图\n",
    "# 一行代码绘图，为了便于介绍，以下将代码分行\n",
    "sns.relplot(   # 使用seaborn.relplot函数制作相关关系图\n",
    "    data=tips,\n",
    "    x = \"消费金额 ($)\",  # 指定x轴变量\n",
    "    y = \"小费金额 ($)\",  # 指定y轴变量\n",
    "    col = \"就餐时间\",    # 通过设置“就餐时间“种类，按照列分面（facets）绘图\n",
    "    hue = \"是否吸烟\",    # 设置散点颜色按照”是否吸烟”种类变化\n",
    "    style = \"客人性别\",  # 设置散点形状按照“是客人性别”变化\n",
    "    palette = [\"#b1283a\",\"#006a8a\"],  # 设置点颜色\n",
    "    s=200  # 设置点大小\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628b378d-516e-4999-9929-7df1cb8db531",
   "metadata": {},
   "source": [
    "2 可视化步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3551b249-fb65-4c1d-8b22-be60ffddf843",
   "metadata": {},
   "source": [
    "可知道seaborn可视化常用4个步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15cd4e74-a970-47d0-accb-6918f68af199",
   "metadata": {},
   "source": [
    "第1步：导入依赖包\n",
    "导入seaborn包并简写为sns。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc446ad8-f135-4125-b690-c804577c7ebd",
   "metadata": {},
   "source": [
    "第2步：设置绘图风格\n",
    "设置绘图风格，例如，“whitegrid”；对坐标轴、图例、网格线等图表属性\r\n",
    "性化设置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed722dea-fafc-4095-9d2d-5188c2d18387",
   "metadata": {},
   "source": [
    "第3步：准备绘图数据\n",
    "seaborn支持多种数据集格式，见章节3.1，可根据可视化数据集的格式灵活\r\n",
    "准备数据，本例中使用pd.read_csv读取3.2.1的tips数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c67ea1-970d-4288-8220-53fc49bb88bc",
   "metadata": {},
   "source": [
    "第4步：挑选seaborn函数绘图\r\n",
    "4.1章节目的在于展示客人的“消费金额 ( )”\r\n",
    "相关关系，以及客人的“就餐时间”、“是否吸烟”、“客人性别”对二者关影\r\n",
    "响，故选择seaborn中展示statistical relationships的函数seaborn.relpot\r\n",
    "函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ceff87f-7ee9-40d4-b6de-2470782acc57",
   "metadata": {},
   "source": [
    "3 绘图函数盘点"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac033e62-3a03-4529-a904-770cb98dca82",
   "metadata": {},
   "source": [
    "从架构层面，seaborn主要有两大类绘图函数，图级别绘图函数（figure\u0002level）、轴级别绘图函数（axes-level），两大类函数异同详细介绍见下文第6\n",
    "章节"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9cd6ee3-416d-45cd-99a4-21e9b54c0f84",
   "metadata": {},
   "source": [
    "美化函数（aesthetics）\r\n",
    "美化图形比例尺度（the scaling of plot elements）、图形风格（th\r\n",
    "general style of the plots）、图形配色（the color palette/colormap）。\r\n",
    "详细介绍见后文第5章节"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60659e4-7e6c-433c-b9ec-5f67f2e35aba",
   "metadata": {},
   "source": [
    "单个图函数\r\n",
    "绘制相关关系图（Relational plots）、分布关系图（Distribution plots）\r\n",
    "分类关系图（Categorical plots）、回归关系图（Regression plots）矩\r\n",
    "阵关系图（Matrix plots）、统计估计和误差棒（Statistical estimation nd\r\n",
    "error bars）。\r\n",
    "详细介绍见后文第7～12章节。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1246b4d1-f378-421d-a7cd-99c7cf71d2a6",
   "metadata": {},
   "source": [
    "组合图函数\r\n",
    "同时展示多个子图表或图形，以便进行比较或展示相关信息。包含分面\r\n",
    "（Facet grids）、配对关系图（Pair grids）、组合关系图（Joint grids）。\r\n",
    "详细介绍见后文第13章节"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2138d4c6-5b2e-4ba8-9830-c68c33df47a0",
   "metadata": {},
   "source": [
    "4 seaborn图形美化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276ffc10-ac01-4e4c-89de-664b756475aa",
   "metadata": {},
   "source": [
    "seaborn中从3个方面美化图形："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6b1c87-8f73-4d10-b337-87a8ccf624a4",
   "metadata": {},
   "source": [
    "比例尺度（context），设置the scaling of plot elements，例如，图中文\r\n",
    "字大小、标记marker大小、线条宽度等；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcc9e06-3010-43d6-89d4-e17cf8bf508b",
   "metadata": {},
   "source": [
    "比例尺度（context），设置the scaling of plot elements，例如，图中文\r\n",
    "字大小、标记marker大小、线条宽度等；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67882dc8-cf7f-46ff-8803-4e305c71917b",
   "metadata": {},
   "source": [
    "配色（palette），设置color palette，例如，Set1、#a1c9f4、red等。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eed603bc-93fd-43c6-a1c4-9a5acfd26b2a",
   "metadata": {},
   "source": [
    "4. 1 context设置图形比例尺度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03e7a4d4-6d27-4735-ade9-eb66dfad51a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'font.size': 10.0,\n",
       " 'axes.labelsize': 'medium',\n",
       " 'axes.titlesize': 'large',\n",
       " 'xtick.labelsize': 'medium',\n",
       " 'ytick.labelsize': 'medium',\n",
       " 'legend.fontsize': 'medium',\n",
       " 'legend.title_fontsize': None,\n",
       " 'axes.linewidth': 0.8,\n",
       " 'grid.linewidth': 0.8,\n",
       " 'lines.linewidth': 1.5,\n",
       " 'lines.markersize': 6.0,\n",
       " 'patch.linewidth': 1.0,\n",
       " 'xtick.major.width': 0.8,\n",
       " 'ytick.major.width': 0.8,\n",
       " 'xtick.minor.width': 0.6,\n",
       " 'ytick.minor.width': 0.6,\n",
       " 'xtick.major.size': 3.5,\n",
       " 'ytick.major.size': 3.5,\n",
       " 'xtick.minor.size': 2.0,\n",
       " 'ytick.minor.size': 2.0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看context内容\n",
    "import seaborn as sns\n",
    "sns.plotting_context()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a00473b6-e590-4ffb-862b-fbc084f4a5ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置context内容\n",
    "# set_context()可设置context，但不会改变图表整体样式\n",
    "# seanborn内置了四套context设置，分别为paper, notebook, talk, poster\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.figure(figsize=(12,8))\n",
    "contexts = ['notebook','paper','talk','poster']\n",
    "for i,context in enumerate(contexts):\n",
    "    sns.set_context(context)  # 设置context\n",
    "    plt.subplot(2,2,i+1)\n",
    "    sns.lineplot(x=[1,2,3,4],y=[3,2,5,1],color='#b1283a')  # 定义折线图，并设置颜色\n",
    "    plt.title(f\"context:\\'{context}'\",color='#006a8e')\n",
    "\n",
    "plt.tight_layout()  # 防止子图重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed75671b-c770-4d41-996b-87eccc09d999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.figure(figsize=(12,8))\n",
    "contexts = ['notebook','paper','talk','poster']\n",
    "for i,context in enumerate(contexts):\n",
    "    sns.set_context('poster',rc={f\"xtick.labelsize\":50})  # 设置context\n",
    "    plt.subplot(2,2,i+1)\n",
    "    sns.lineplot(x=[1,2,3,4],y=[3,2,5,1],color='#b1283a')  # 定义折线图，并设置颜色\n",
    "    plt.title(f\"context:\\'{context}'\",color='#006a8e')\n",
    "\n",
    "plt.tight_layout()  # 防止子图重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "00e8cbe5-ee60-4a2a-afeb-45ac6ac28a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可通过 font_scale 设置整个图的字体缩放\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.figure(figsize=(12,8))\n",
    "contexts = ['notebook','paper','talk','poster']\n",
    "for i,context in enumerate(contexts):\n",
    "    sns.set_context('poster',font_scale=2)  # 设置context\n",
    "    plt.subplot(2,2,i+1)\n",
    "    sns.lineplot(x=[1,2,3,4],y=[3,2,5,1],color='#b1283a')  # 定义折线图，并设置颜色\n",
    "    plt.title(f\"context:\\'{context}'\",color='#006a8e')\n",
    "\n",
    "plt.tight_layout()  # 防止子图重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad317bb-bdb9-4b6e-b4cb-c77db94895cf",
   "metadata": {},
   "source": [
    "4.2 style设置图形通用样式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7dfcc636-ac75-4f4e-85fe-dacfa84dce3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'axes.facecolor': 'white',\n",
       " 'axes.edgecolor': 'black',\n",
       " 'axes.grid': False,\n",
       " 'axes.axisbelow': 'line',\n",
       " 'axes.labelcolor': 'black',\n",
       " 'figure.facecolor': 'white',\n",
       " 'grid.color': '#b0b0b0',\n",
       " 'grid.linestyle': '-',\n",
       " 'text.color': 'black',\n",
       " 'xtick.color': 'black',\n",
       " 'ytick.color': 'black',\n",
       " 'xtick.direction': 'out',\n",
       " 'ytick.direction': 'out',\n",
       " 'lines.solid_capstyle': <CapStyle.projecting: 'projecting'>,\n",
       " 'patch.edgecolor': 'black',\n",
       " 'patch.force_edgecolor': False,\n",
       " 'image.cmap': 'viridis',\n",
       " 'font.family': ['sans-serif'],\n",
       " 'font.sans-serif': ['DejaVu Sans',\n",
       "  'Bitstream Vera Sans',\n",
       "  'Computer Modern Sans Serif',\n",
       "  'Lucida Grande',\n",
       "  'Verdana',\n",
       "  'Geneva',\n",
       "  'Lucid',\n",
       "  'Arial',\n",
       "  'Helvetica',\n",
       "  'Avant Garde',\n",
       "  'sans-serif'],\n",
       " 'xtick.bottom': True,\n",
       " 'xtick.top': False,\n",
       " 'ytick.left': True,\n",
       " 'ytick.right': False,\n",
       " 'axes.spines.left': True,\n",
       " 'axes.spines.bottom': True,\n",
       " 'axes.spines.right': True,\n",
       " 'axes.spines.top': True}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看style内容\n",
    "import seaborn as sns\n",
    "sns.axes_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60ddccca-d0d8-48b8-abb3-e27eed12d870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置style内容\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt  \n",
    "plt.figure(figsize=(12,8))\n",
    "sns.set_theme()\n",
    "styles = ['darkgrid','whitegrid','dark','white','ticks']\n",
    "for i,style in enumerate(styles):\n",
    "    sns.set_style(style)  # 设置style\n",
    "    plt.subplot(2,3,i+1)\n",
    "    sns.lineplot(x=[1,2,3,4],y=[3,2,5,1],color='#b1283a')  # 定义折线图，并设置颜色\n",
    "    plt.title(f\"context:\\'{style}'\",color='#006a8e')\n",
    "\n",
    "plt.tight_layout()  # 防止子图重叠\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a92aa57-cd2a-41ca-afee-e2d91d806395",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m sns\u001b[38;5;241m.\u001b[39mset_style(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwhitegrid\u001b[39m\u001b[38;5;124m'\u001b[39m,rc\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgrid.color\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#6ccea3\u001b[39m\u001b[38;5;124m'\u001b[39m})\n\u001b[0;32m      9\u001b[0m sns\u001b[38;5;241m.\u001b[39mlineplot(x\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m4\u001b[39m],y\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m1\u001b[39m],color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#b1283a\u001b[39m\u001b[38;5;124m'\u001b[39m)  \u001b[38;5;66;03m# 定义折线图，并设置颜色\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m plt(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mset_style with \u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124mrc\u001b[39m\u001b[38;5;130;01m\\\"\u001b[39;00m\u001b[38;5;124m'\u001b[39m,color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#006a8e\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 上图可清晰的展示随着style变化，图形中背景色、网格线等属性的变化\n",
    "# 在使用某款已经内置的style方案的同时，还可以通过rc独立设置某个参数\n",
    "# 给网格线换个颜色\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# rc设置网格线的颜色为绿色\n",
    "sns.set_style('whitegrid',rc={'grid.color':'#6ccea3'})\n",
    "sns.lineplot(x=[1,2,3,4],y=[3,2,5,1],color='#b1283a')  # 定义折线图，并设置颜色\n",
    "plt('set_style with \\\"rc\\\"',color='#006a8e')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bedb2ab7-e520-4d4c-91c3-8cbec89a3f1c",
   "metadata": {},
   "source": [
    "4.3 palette设置图形配色"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef4bf51-c0c4-4dd8-8206-bf4984625d7d",
   "metadata": {},
   "source": [
    "设置图形配色palette目的在于有效地展示数据、让图表吸引人，seaborn调色\r\n",
    "板palette可分为3大类："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece61c11-e09a-4b78-84b0-423d1c8aaa6a",
   "metadata": {},
   "source": [
    "定性调色板（qualitative palettes）：适用于分类数据（categorical\n",
    "data），例如，一项调查中的满意度评级（“非常不满意”，“不满意”，“\r\n",
    "立”，“满意”，“非常满意”）中人员占比数据，"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c02ff3-cff7-4b5d-b17c-73ca9865b5f0",
   "metadata": {},
   "source": [
    "顺序调色板（sequential palettes）：适用于数值数据（numeric data），\r\n",
    "例如，一周中每天温度值数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e5b6e0-cbbc-43be-9970-b811d850ddc4",
   "metadata": {},
   "source": [
    "离散调色板（diverging palettes）：适用于在数值数据中表示具有分类边界\n",
    "的情况（numeric data with a categorical boundary），例如，小编粉\r\n",
    "中12-18岁、19-24岁、25-35岁各区间人员占比数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac95e30-3d13-4789-9241-b8abde0884a3",
   "metadata": {},
   "source": [
    "4.3.1构建颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "de60e367-4621-4654-9845-c51f32282c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edd1cb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#daa4ac;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bd7a98;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#945785;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#613969;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2d1e3e;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],\n",
       " [0.8559578605899612, 0.6418993116910497, 0.6754191211563135],\n",
       " [0.739734329496642, 0.4765280683170713, 0.5959617419736206],\n",
       " [0.57916573903086, 0.33934576125314425, 0.5219003947563425],\n",
       " [0.37894937987024996, 0.2224702044652721, 0.41140014301575434],\n",
       " [0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cubehelix_palette\n",
    "'''\n",
    "基于cubehelix系统使用cubehelix_palette函数构建sequential palettes，生成\n",
    "一个亮度线性递减（或递增）的colormap，使用该颜色生成的图形无论是打印\n",
    "成黑白图像或被色盲者观看时都依然能够清晰展现数据的不同特征\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "cubehelix_palette_test = sns.cubehelix_palette(\n",
    "    n_colors=6,  # 调色板中的颜色数量\n",
    "    start=0,  # 设置颜色的起始值\n",
    "    rot=0.4,  # 设置颜色轮上的旋转\n",
    "    gamma=1.0, # 调整颜色的非线性以突出暗色（gamma<1）或亮色（gamma>1）\n",
    "    hue=0.8, # 设置颜色饱和度（0<=hue<=1）\n",
    "    light=0.85, # 设置颜色的最暗颜色的强度（0 <= light <= 1）\n",
    "    dark=0.15, # 设置颜色的最亮颜色的强度0 <= dark <= 1\n",
    "    reverse=False, # 默颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False # False返回元组格式颜色\n",
    ")\n",
    "cubehelix_palette_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "18af6293-15b1-4c1f-b474-0f858be7dcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edd1cb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e4b8b8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d89faa;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c8879e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b47194;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9e5e8a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#834c7d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#673c6d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#492d58;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2d1e3e;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],\n",
       " [0.893122085710277, 0.7206100946592334, 0.72229576813508],\n",
       " [0.8454263141680334, 0.6225806657605325, 0.6651572981499156],\n",
       " [0.7840440880599453, 0.5292660544265891, 0.6200568926941761],\n",
       " [0.7070159397024951, 0.4429345424186431, 0.5801722574606395],\n",
       " [0.6196337473419887, 0.3684648209452393, 0.5405540573846532],\n",
       " [0.5151069036855755, 0.29801047535056074, 0.49050619139300705],\n",
       " [0.402075529973261, 0.23451699199015608, 0.4263168000834109],\n",
       " [0.28645868165429034, 0.17570375961380752, 0.3440754090644915],\n",
       " [0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cubehelix_palette_test = sns.cubehelix_palette(\n",
    "    n_colors=10,  # 颜色数量         !!!!!!!!!!!!!!!!!\n",
    "    start=0,  # 设置颜色的起始值\n",
    "    rot=0.4,  # 设置颜色轮上的旋转值\n",
    "    gamma=1.0, # 调整颜色的非线性以突出暗色（gamma<1）或亮色（gamma>1）\n",
    "    hue=0.8, # 设置颜色饱和度（0<=hue<=1）\n",
    "    light=0.85, # 设置颜色的最暗颜色的强度（0 <= light <= 1）\n",
    "    dark=0.15, # 设置颜色的最亮颜色的强度0 <= dark <= 1\n",
    "    reverse=False, # 默颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False # False返回元组格式颜色\n",
    ")\n",
    "cubehelix_palette_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6320dcf9-1362-46e3-a237-9b62878a34d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e3d0ef;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2a7da;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9d82bf;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#76619e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4e4173;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#26213f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[[0.8905754254448687, 0.8147931586177104, 0.9370899823648525],\n",
       " [0.7621164030526003, 0.6558619269044483, 0.8564601188363787],\n",
       " [0.6161408029004956, 0.5106567966889349, 0.7499806033843956],\n",
       " [0.4641552653302901, 0.3799305249891585, 0.6178828506862256],\n",
       " [0.3047893259484756, 0.2529712804196341, 0.45005766104680045],\n",
       " [0.14996923688455951, 0.1299600504140209, 0.24865883270492517]]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cubehelix_palette_test = sns.cubehelix_palette(\n",
    "    n_colors=6,  # 颜色数量\n",
    "    start=0,  # 设置颜色的起始值\n",
    "    rot=0.1,  # 设置颜色轮上的旋转值     !!!!!!!!!!!!!!!!!!!!!\n",
    "    gamma=1.0, # 调整颜色的非线性以突出暗色（gamma<1）或亮色（gamma>1）\n",
    "    hue=0.8, # 设置颜色饱和度（0<=hue<=1）\n",
    "    light=0.85, # 设置颜色的最暗颜色的强度（0 <= light <= 1）\n",
    "    dark=0.15, # 设置颜色的最亮颜色的强度0 <= dark <= 1\n",
    "    reverse=False, # 默颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False # False返回元组格式颜色\n",
    ")\n",
    "cubehelix_palette_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f02984c5-a290-47d1-9829-7511b01a7467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d9dec0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c9b290;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b88276;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9b586b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6a365d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#311d3c;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[[0.8524902186077546, 0.8688298366365058, 0.7511241897976625],\n",
       " [0.7867034087905997, 0.697782164089918, 0.5645582857626474],\n",
       " [0.7235228374372512, 0.5094303167711178, 0.46369828190115114],\n",
       " [0.6068302176306956, 0.3441569275733417, 0.4206461510535002],\n",
       " [0.4150829618663051, 0.21278594439773885, 0.3647969396172203],\n",
       " [0.19081288660118892, 0.11197819021591968, 0.2337155290699754]]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "cubehelix_palette_test = sns.cubehelix_palette(\n",
    "    n_colors=6,  # 颜色数量\n",
    "    start=0,  # 设置颜色的起始值\n",
    "    rot=0.6,  # 设置颜色轮上的旋转值   !!!!!!!!!!!!!!!!!!!!!!\n",
    "    gamma=1.0, # 调整颜色的非线性以突出暗色（gamma<1）或亮色（gamma>1）\n",
    "    hue=0.8, # 设置颜色饱和度（0<=hue<=1）\n",
    "    light=0.85, # 设置颜色的最暗颜色的强度（0 <= light <= 1）\n",
    "    dark=0.15, # 设置颜色的最亮颜色的强度0 <= dark <= 1\n",
    "    reverse=False, # 默颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False # False返回元组格式颜色\n",
    ")\n",
    "cubehelix_palette_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3fb968bf-a89d-4e39-b35f-90c2a77d2712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#262626;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3e3e3e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#555555;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6e6e6e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#868686;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9f9f9f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b7b7b7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cfcfcf;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e7e7e7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffffff;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.1477136475930195, 0.14772002848141771, 0.1476949000552352),\n",
       " (0.24129803138672717, 0.24130371162855616, 0.24128134240211133),\n",
       " (0.3348824151804348, 0.3348873947756946, 0.3348677847489875),\n",
       " (0.4318090983953463, 0.4318133523209451, 0.4317966000368234),\n",
       " (0.525393482189054, 0.5253970354680836, 0.5253830423836996),\n",
       " (0.6223201654039655, 0.6223229930133342, 0.6223118576715356),\n",
       " (0.7159045491976731, 0.7159066761604725, 0.7158983000184117),\n",
       " (0.8128312324125847, 0.8128326337057231, 0.8128271153062477),\n",
       " (0.9064156162062923, 0.9064163168528615, 0.9064135576531238),\n",
       " (1.0, 1.0, 1.0)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dark_palette\n",
    "'''\n",
    "混合深色和指定颜色，使用dark_palette函数构建sequential palettes，生成一个深色到指定色的colormap\n",
    "适用场景：适用于数据的范围在感兴趣的高值数据～不感兴趣的低值数据之间的变化情况\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "# 创建一个由白色渐变到暗色的颜色调色板\n",
    "palette = sns.dark_palette(\n",
    "    color='white', # 渐变色的终点颜色\n",
    "    n_colors=10,  # 调色板中的颜色数量\n",
    "    reverse=False,  # 颜色顺序，false表示从暗到亮\n",
    "    as_cmap=False, # 返回元组格式的颜色，如果设置为true则返回matplotlib colormap\n",
    "    input='rgb' # 输入颜色系统的类型\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "72f3cca3-8121-40a4-825f-235087d22e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#232625;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#29352e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2f4338;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#355143;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3b5f4d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#416e57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#477c61;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4d8b6b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#539975;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#59a77f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.13833421404802923, 0.15081790185204338, 0.1433410872922536),\n",
       " (0.16146829650396266, 0.20616830832303779, 0.1822883327629696),\n",
       " (0.1846023789598961, 0.26151871479403216, 0.22123557823368561),\n",
       " (0.20856267864639855, 0.3188459214961335, 0.2615737967569272),\n",
       " (0.231696761102332, 0.3741963279671279, 0.30052104222764325),\n",
       " (0.2556570607888345, 0.4315235346692292, 0.34085926075088485),\n",
       " (0.2787911432447679, 0.48687394114022364, 0.37980650622160084),\n",
       " (0.3027514429312704, 0.544201147842325, 0.4201447247448425),\n",
       " (0.32588552538720383, 0.5995515543133193, 0.4590919702155585),\n",
       " (0.34901960784313724, 0.6549019607843137, 0.4980392156862745)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改终止色名称color\n",
    "import seaborn as sns\n",
    "palette = sns.dark_palette(\n",
    "    color='#59a77f', # 渐变色的终点颜色          !!!!!!!!!!!!!!!!!!!\n",
    "    n_colors=10,  # 调色板中的颜色数量\n",
    "    reverse=False,  # 颜色顺序，false表示从暗到亮\n",
    "    as_cmap=False, # 返回元组格式的颜色，如果设置为true则返回matplotlib colormap\n",
    "    input='rgb' # 输入颜色系统的类型\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d1382e56-7f4b-4225-b7d1-40d031f1ff6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffffff;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e7e7e7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cfcfcf;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b7b7b7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9f9f9f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#868686;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6e6e6e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#555555;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3e3e3e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#262626;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(1.0, 1.0, 1.0),\n",
       " (0.9064156162062923, 0.9064163168528616, 0.9064135576531238),\n",
       " (0.8128312324125847, 0.8128326337057231, 0.8128271153062477),\n",
       " (0.7159045491976732, 0.7159066761604727, 0.7158983000184118),\n",
       " (0.6223201654039655, 0.6223229930133342, 0.6223118576715356),\n",
       " (0.525393482189054, 0.5253970354680836, 0.5253830423836996),\n",
       " (0.4318090983953464, 0.4318133523209452, 0.4317966000368235),\n",
       " (0.3348824151804348, 0.3348873947756946, 0.3348677847489875),\n",
       " (0.24129803138672723, 0.24130371162855624, 0.2412813424021114),\n",
       " (0.1477136475930195, 0.14772002848141771, 0.1476949000552352)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从亮到暗\n",
    "import seaborn as sns\n",
    "# 创建一个由白色渐变到暗色的颜色调色板\n",
    "palette = sns.dark_palette(\n",
    "    color='white', # 渐变色的终点颜色\n",
    "    n_colors=10,  # 调色板中的颜色数量\n",
    "    reverse=True,  # 颜色顺序，false表示从暗到亮\n",
    "    as_cmap=False, # 返回元组格式的颜色，如果设置为true则返回matplotlib colormap\n",
    "    input='rgb' # 输入颜色系统的类型\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "23daff5f-eda9-445a-86d3-65d2ad08b280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2b2423;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#47302b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#633b33;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f463a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9b5142;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b85d4a;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.16784269928290113, 0.1420671093901477, 0.1388943730951417),\n",
       " (0.27831348977675757, 0.18638106638138052, 0.16898525031516964),\n",
       " (0.388784280270614, 0.23069502337261336, 0.19907612753519763),\n",
       " (0.49925507076447045, 0.2750089803638462, 0.22916700475522558),\n",
       " (0.609725861258327, 0.319322937355079, 0.25925788197525357),\n",
       " (0.7201966517521833, 0.36363689434631186, 0.2893487591952815)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改input为husl颜色系统\n",
    "import seaborn as sns\n",
    "\n",
    "palette = sns.dark_palette(\n",
    "    color=(20,60,50),\n",
    "    input='husl'\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e15c82d7-be1c-4e18-86a9-8e97d96dc161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f0f0f3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6d6f4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbcf5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a0a0f7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8686f8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6b6bfa;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5050fb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3535fc;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1a1afe;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#0000ff;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9427942677547513, 0.942825384792593, 0.9519953287278279),\n",
       " (0.839271759922857, 0.8392994601879161, 0.9572664298871252),\n",
       " (0.7357492520909628, 0.7357735355832393, 0.9625375310464226),\n",
       " (0.6285295118365009, 0.6285502565283954, 0.9679968858185519),\n",
       " (0.5250070040046066, 0.5250243319237184, 0.9732679869778492),\n",
       " (0.41778726375014463, 0.4178010528688746, 0.9787273417499787),\n",
       " (0.3142647559182504, 0.3142751282641977, 0.983998442909276),\n",
       " (0.20704501566378852, 0.2070518492093537, 0.9894577976814053),\n",
       " (0.10352250783189432, 0.10352592460467691, 0.9947288988407027),\n",
       " (0.0, 0.0, 1.0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# light_palette\n",
    "'''\n",
    "混合浅色和指定颜色，使用light_palette函数构建sequential palettes，生成一\n",
    "个浅色到指定色的colormap\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "palette = sns.light_palette(\n",
    "    color='blue',  # 传入一个颜色字符串或者颜色的RGB值，作为生成渐变色的终点\n",
    "    n_colors=10,  # colormap数量\n",
    "    reverse=False,  # 设置颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False  # false返回元组格式颜色\n",
    "    \n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae2c1443-e5f9-4c07-ac4f-9ef98728bd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f2f0f0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#eedbda;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e9c5c4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e4afae;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#df9a98;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#da8481;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d56f6b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d15955;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cc433f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c72e29;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9507700602644528, 0.9415244907176071, 0.9413124901936948),\n",
       " (0.9320618983223045, 0.8579491756276615, 0.8556074355226763),\n",
       " (0.9133537363801562, 0.7743738605377158, 0.7699023808516576),\n",
       " (0.8939774257972168, 0.6878137127659865, 0.6811364313709599),\n",
       " (0.8752692638550685, 0.6042383976760408, 0.5954313766999414),\n",
       " (0.8558929532721293, 0.5176782499043113, 0.5066654272192436),\n",
       " (0.837184791329981, 0.4341029348143658, 0.42096037254822505),\n",
       " (0.8178084807470417, 0.3475427870426364, 0.3321944230675272),\n",
       " (0.7991003188048934, 0.26396747195269077, 0.2464893683965088),\n",
       " (0.7803921568627451, 0.1803921568627451, 0.1607843137254902)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 同dark_palette，light_palette实际使用时，只需要修改个别参数，大部分都可以直接使用默认值\n",
    "# 修改color\n",
    "import seaborn as sns\n",
    "palette = sns.light_palette(\n",
    "    color='#c72e29',  # 传入一个颜色字符串或者颜色的RGB值，作为生成渐变色的终点\n",
    "    n_colors=10,  # colormap数量\n",
    "    reverse=False,  # 设置颜色顺序，False为从亮到暗，True为从暗到亮\n",
    "    as_cmap=False  # false返回元组格式颜色\n",
    "    \n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6bfd9ec-3da3-4b91-a720-04603a598c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6a3d9a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cab2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f00;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdbf6f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e31a1c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fb9a99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#33a02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b2df8a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f78b4;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(1.0, 1.0, 0.6),\n",
       " (0.41568627450980394, 0.23921568627450981, 0.6039215686274509),\n",
       " (0.792156862745098, 0.6980392156862745, 0.8392156862745098),\n",
       " (1.0, 0.4980392156862745, 0.0),\n",
       " (0.9921568627450981, 0.7490196078431373, 0.43529411764705883),\n",
       " (0.8901960784313725, 0.10196078431372549, 0.10980392156862745),\n",
       " (0.984313725490196, 0.6039215686274509, 0.6),\n",
       " (0.2, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.6980392156862745, 0.8745098039215686, 0.5411764705882353),\n",
       " (0.12156862745098039, 0.47058823529411764, 0.7058823529411765)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mpl_palette\n",
    "'''\n",
    "基于matplotlib的colormap，使用mpl_palette函数构建diverging/sequential\n",
    "palettes，生成一个离散或者连续的colormap。\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# 从matplotlib colormap中创建调色板\n",
    "palette = sns.mpl_palette(\n",
    "    name = 'Paired_r',  # 设置matplotlib colormap的名称\n",
    "    n_colors=10,  # 调色板中颜色数量\n",
    "    as_cmap=False\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f0ab6d0-2edc-4aa6-89ba-921c6d8dc5f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#eaf7e6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d8f0d2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c1e6ba;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a4da9e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#84cc83;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#62bb6d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3fa85b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#289049;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#107a37;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#006227;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9176931949250289, 0.9684275278738947, 0.901361014994233),\n",
       " (0.8459054209919262, 0.9399307958477509, 0.8228219915417148),\n",
       " (0.7558477508650518, 0.9033910034602076, 0.7290426758938869),\n",
       " (0.6436447520184544, 0.8561476355247981, 0.6197923875432525),\n",
       " (0.5185697808535179, 0.7983391003460207, 0.5150941945405614),\n",
       " (0.3827450980392157, 0.7332564398308343, 0.42737408688965783),\n",
       " (0.2452133794694348, 0.6602537485582468, 0.35695501730103807),\n",
       " (0.1566320645905421, 0.5657670126874279, 0.28608996539792386),\n",
       " (0.06082276047673972, 0.47958477508650516, 0.21599384851980008),\n",
       " (0.0, 0.38268358323721646, 0.15398692810457515)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重点强调name参数：\n",
    "# 当name选择matplotlib中diverging型调色盘时，如Paired_r，生成也为diverging型颜色\n",
    "# 当name选择matplotlib中sequential型调色盘时，如Greens，生成也为sequential型颜色\n",
    "# 修改name为sequential型调色板greens\n",
    "import seaborn as sns\n",
    "\n",
    "# 从matplotlib colormap中创建调色板\n",
    "palette = sns.mpl_palette(\n",
    "    name = 'Greens',  # 设置matplotlib colormap的名称\n",
    "    n_colors=10,  # 调色板中颜色数量\n",
    "    as_cmap=False\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "903a96de-7699-4297-8764-56ae25cc714e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#006227;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#107a37;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#289049;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3fa85b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#62bb6d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#84cc83;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a4da9e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c1e6ba;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d8f0d2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#eaf7e6;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.0, 0.3826835832372164, 0.15398692810457515),\n",
       " (0.06082276047673972, 0.47958477508650516, 0.21599384851980008),\n",
       " (0.1566320645905421, 0.5657670126874279, 0.28608996539792386),\n",
       " (0.24521337946943483, 0.6602537485582468, 0.35695501730103807),\n",
       " (0.38274509803921564, 0.7332564398308343, 0.42737408688965783),\n",
       " (0.5185697808535179, 0.7983391003460207, 0.5150941945405613),\n",
       " (0.6436447520184544, 0.8561476355247981, 0.6197923875432525),\n",
       " (0.7558477508650518, 0.9033910034602076, 0.7290426758938869),\n",
       " (0.8459054209919262, 0.9399307958477509, 0.8228219915417148),\n",
       " (0.9176931949250289, 0.9684275278738946, 0.9013610149942329)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# greens的逆颜色为greens_r\n",
    "import seaborn as sns\n",
    "\n",
    "# 从matplotlib colormap中创建调色板\n",
    "palette = sns.mpl_palette(\n",
    "    name = 'Greens_r',  # 设置matplotlib colormap的名称\n",
    "    n_colors=10,  # 调色板中颜色数量\n",
    "    as_cmap=False\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd42e920-6135-4fed-8a5a-36df65132290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1814b8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7a14b8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b81494;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b81432;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b85914;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b4b814;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#52b814;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#14b838;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#14b89a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1473b8;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.09280000000000026, 0.07999999999999996, 0.7200000000000001),\n",
       " (0.4767999999999998, 0.07999999999999996, 0.7200000000000001),\n",
       " (0.7200000000000001, 0.07999999999999996, 0.5791999999999992),\n",
       " (0.7200000000000001, 0.07999999999999996, 0.19519999999999965),\n",
       " (0.7200000000000001, 0.3488000000000002, 0.07999999999999996),\n",
       " (0.7072000000000005, 0.7200000000000001, 0.07999999999999996),\n",
       " (0.32320000000000004, 0.7200000000000001, 0.07999999999999996),\n",
       " (0.07999999999999996, 0.7200000000000001, 0.22080000000000047),\n",
       " (0.07999999999999996, 0.7200000000000001, 0.6048000000000009),\n",
       " (0.07999999999999996, 0.45119999999999966, 0.7200000000000001)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hls_palette\n",
    "'''\n",
    "用HLS颜色系统使用hls_palette函数构建恒定亮度和饱和度的palettes，生成适\n",
    "用于categorical or cyclical类数据的colormap。\n",
    "HSL颜色系统由色相（Hue）、饱和度（Saturation）和亮度（Lightness）三个\n",
    "要素组成。色相H表示颜色的种类或者说是色彩的名称（比如红色、绿色、蓝色\n",
    "等），饱和度S表示颜色的纯度或者说是灰度的程度，而亮度L则表示颜色的明\n",
    "暗程度\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# 生成调色板\n",
    "palette = sns.hls_palette(\n",
    "    n_colors=10,  # colormap颜色数量\n",
    "    h=0.67,       # 设置颜色色相，h沿着一个圆形路径均匀采样，0.67（120/360度）代表蓝色\n",
    "    l=0.4,        # 设置颜色亮度\n",
    "    s=0.8,        # 设置颜色饱和度\n",
    "    as_cmap=False      # 返回列表而不是colormap对象\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef1d8f1b-892c-4696-9bec-3958af8e94c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db5757;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dba657;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c1db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#71db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57db8c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57dbdb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#578cdb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7157db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c157db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db57a6;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.86, 0.33999999999999997, 0.33999999999999997),\n",
       " (0.86, 0.652, 0.33999999999999997),\n",
       " (0.7559999999999999, 0.86, 0.33999999999999997),\n",
       " (0.44399999999999995, 0.86, 0.33999999999999997),\n",
       " (0.33999999999999997, 0.86, 0.548),\n",
       " (0.33999999999999997, 0.8599999999999999, 0.86),\n",
       " (0.33999999999999997, 0.5479999999999996, 0.86),\n",
       " (0.4440000000000003, 0.33999999999999997, 0.86),\n",
       " (0.7559999999999999, 0.33999999999999997, 0.86),\n",
       " (0.86, 0.33999999999999997, 0.6519999999999999)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改hsl参数，颜色随之变化\n",
    "# 设置h色相为红，即为0\n",
    "sns.hls_palette(n_colors=10,h=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf13ca81-d69e-4aff-9a13-e106cd2353a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edabab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edd3ab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e0edab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b8edab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abedc5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abeded;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abc5ed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b8abed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e0abed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edabd3;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9300000000000002, 0.6699999999999999, 0.6699999999999999),\n",
       " (0.9300000000000002, 0.8260000000000001, 0.6699999999999999),\n",
       " (0.8780000000000001, 0.9300000000000002, 0.6699999999999999),\n",
       " (0.722, 0.9300000000000002, 0.6699999999999999),\n",
       " (0.6699999999999999, 0.9300000000000002, 0.7740000000000001),\n",
       " (0.6699999999999999, 0.9300000000000002, 0.9300000000000002),\n",
       " (0.6699999999999999, 0.7739999999999998, 0.9300000000000002),\n",
       " (0.7220000000000001, 0.6699999999999999, 0.9300000000000002),\n",
       " (0.8780000000000001, 0.6699999999999999, 0.9300000000000002),\n",
       " (0.9300000000000002, 0.6699999999999999, 0.8260000000000001)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置亮度l\n",
    "sns.hls_palette(n_colors=10,h=0,l=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f10c74db-a6e7-47ec-a0ff-5bf55a4b26d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6c2c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6cec2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d2d6c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c6d6c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2d6ca;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2d6d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2cad6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c6c2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d2c2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6c2ce;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.84, 0.7600000000000001, 0.7600000000000001),\n",
       " (0.84, 0.808, 0.7600000000000001),\n",
       " (0.824, 0.84, 0.7600000000000001),\n",
       " (0.7760000000000001, 0.84, 0.7600000000000001),\n",
       " (0.7600000000000001, 0.84, 0.792),\n",
       " (0.7600000000000001, 0.84, 0.84),\n",
       " (0.7600000000000001, 0.792, 0.84),\n",
       " (0.7760000000000001, 0.7600000000000001, 0.84),\n",
       " (0.824, 0.7600000000000001, 0.84),\n",
       " (0.84, 0.7600000000000001, 0.808)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置饱和度s\n",
    "sns.hls_palette(n_colors=10,h=0,l=0.8,s=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "59a9fe00-6b13-4c0a-ba69-01b9e9a7d838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2e6287;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7638cc;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9a3092;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a82b67;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#944827;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#715a27;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#576227;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#276a36;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#29675a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2b666c;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.1790201477868881, 0.38320704988399945, 0.5304294596442976),\n",
       " (0.46194107070472606, 0.21810170250620248, 0.8019440633780046),\n",
       " (0.6030731663861281, 0.18694916467106723, 0.5726498141926499),\n",
       " (0.6574369710979572, 0.16988488251910483, 0.4030795533090519),\n",
       " (0.5789991431511188, 0.2813297744028449, 0.15401576851982973),\n",
       " (0.44319574124438316, 0.35392704300910105, 0.15242906613708937),\n",
       " (0.34304728195333145, 0.3860537074392251, 0.15157865771953488),\n",
       " (0.15295249058388377, 0.4158800715655456, 0.21011490580991987),\n",
       " (0.16182319540608364, 0.4057183551566542, 0.35410563650913524),\n",
       " (0.1679375336634629, 0.39816564410445937, 0.4250826539033569)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# husl_palette\n",
    "'''\n",
    "用HUSL颜色系统使用husl_palette函数构建恒定亮度和饱和度的palettes，生成\n",
    "适用于categorical or cyclical类数据的colormap。\n",
    "HUSL（Human-friendly HSL）颜色系统是HSL的改进版本，提供更加人类友好\n",
    "的颜色，HUSL考虑了人眼对颜色的感知方式，更好地平衡了色相、饱和度和亮\n",
    "度之间的关系，这使得在图形和数据可视化中使用HUSL配色更吸引人\n",
    "\n",
    "husl_palette使用方法，和hsl_palette使用方法完全一样\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "# 生成调色板\n",
    "palette = sns.husl_palette(\n",
    "    n_colors=10,  # colormap颜色数量\n",
    "    h=0.67,       # 设置颜色色相，h沿着一个圆形路径均匀采样，0.67（120/360度）代表蓝色\n",
    "    l=0.4,        # 设置颜色亮度\n",
    "    s=0.8,        # 设置颜色饱和度\n",
    "    as_cmap=False      # 返回列表而不是colormap对象\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b0f7d69-f16f-4941-80fb-78788832fd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db5757;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dba657;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c1db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#71db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57db8c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57dbdb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#578cdb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7157db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c157db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db57a6;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.86, 0.33999999999999997, 0.33999999999999997),\n",
       " (0.86, 0.652, 0.33999999999999997),\n",
       " (0.7559999999999999, 0.86, 0.33999999999999997),\n",
       " (0.44399999999999995, 0.86, 0.33999999999999997),\n",
       " (0.33999999999999997, 0.86, 0.548),\n",
       " (0.33999999999999997, 0.8599999999999999, 0.86),\n",
       " (0.33999999999999997, 0.5479999999999996, 0.86),\n",
       " (0.4440000000000003, 0.33999999999999997, 0.86),\n",
       " (0.7559999999999999, 0.33999999999999997, 0.86),\n",
       " (0.86, 0.33999999999999997, 0.6519999999999999)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 其它参数修改请参考5.3.1.5 hsl_palette章节，这里比较husl_palette和hsl_palette区别\n",
    "# hls_palette中设置色相\n",
    "sns.hls_palette(n_colors=10,h=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1948a64-5d4e-4c8c-872d-a1df11d0c8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edabab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edd3ab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e0edab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b8edab;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abedc5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abeded;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#abc5ed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b8abed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e0abed;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edabd3;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9300000000000002, 0.6699999999999999, 0.6699999999999999),\n",
       " (0.9300000000000002, 0.8260000000000001, 0.6699999999999999),\n",
       " (0.8780000000000001, 0.9300000000000002, 0.6699999999999999),\n",
       " (0.722, 0.9300000000000002, 0.6699999999999999),\n",
       " (0.6699999999999999, 0.9300000000000002, 0.7740000000000001),\n",
       " (0.6699999999999999, 0.9300000000000002, 0.9300000000000002),\n",
       " (0.6699999999999999, 0.7739999999999998, 0.9300000000000002),\n",
       " (0.7220000000000001, 0.6699999999999999, 0.9300000000000002),\n",
       " (0.8780000000000001, 0.6699999999999999, 0.9300000000000002),\n",
       " (0.9300000000000002, 0.6699999999999999, 0.8260000000000001)]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hls_palette中设置亮度\n",
    "sns.hls_palette(n_colors=10,h=0,l=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "704b6ca6-b332-43f9-b70d-6184da0be4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6c2c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6cec2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d2d6c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c6d6c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2d6ca;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2d6d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c2cad6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c6c2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d2c2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d6c2ce;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.84, 0.7600000000000001, 0.7600000000000001),\n",
       " (0.84, 0.808, 0.7600000000000001),\n",
       " (0.824, 0.84, 0.7600000000000001),\n",
       " (0.7760000000000001, 0.84, 0.7600000000000001),\n",
       " (0.7600000000000001, 0.84, 0.792),\n",
       " (0.7600000000000001, 0.84, 0.84),\n",
       " (0.7600000000000001, 0.792, 0.84),\n",
       " (0.7760000000000001, 0.7600000000000001, 0.84),\n",
       " (0.824, 0.7600000000000001, 0.84),\n",
       " (0.84, 0.7600000000000001, 0.808)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# hls_palette中设置饱和度\n",
    "sns.hls_palette(n_colors=10,h=0,l=0.8,s=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b217caf4-9d10-47f5-8738-59f88399474b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d53d69;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e185a0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#edcdd6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ceddc8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8eb080;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4f8438;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.8340245009323628, 0.237592525883977, 0.413389203308121),\n",
       " (0.8806176189679763, 0.5202959033482922, 0.6259841195095547),\n",
       " (0.9276657479219064, 0.8057600559831574, 0.8406551579395181),\n",
       " (0.8081510223014702, 0.8668800710164802, 0.7858748468802361),\n",
       " (0.5582878328647862, 0.6910771288388899, 0.5022164042219127),\n",
       " (0.310841115279521, 0.516974408539226, 0.221301273388138)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# diverging_palette\n",
    "'''\n",
    "用HUSL颜色系统使用diverging_palette函数构建diverging palettes，生成适用\n",
    "于diverging类数据的colormap。\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "# 生成发散调色板\n",
    "palette = sns.diverging_palette(\n",
    "    h_neg=0,  # 设置左端颜色色相（0<=h_neg<=359）\n",
    "    h_pos=120, # 设置右端颜色色相（0<=h_pos<=359）\n",
    "    s=75,  # 设置颜色饱和度（0<=s<=100）\n",
    "    l=50,  # 设置颜色亮度（0<=l<=100）\n",
    "    sep=1,  # 介于h_neg h_pos之间区域大小\n",
    "    n=6,   # colormap颜色数量\n",
    "    center='light',  # 构建的colormap中间色调，light dark可选\n",
    "    as_cmap=False  # 返回列表而不是colormap对象\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a30c221-06a2-42d1-910d-1d04339fc391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4f8438;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#729c60;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#95b587;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b9ceb1;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dce7d8;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#efdde2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e9b5c4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e28ca5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db6487;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d53d69;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.310841115279521, 0.516974408539226, 0.221301273388138),\n",
       " (0.4466942151302549, 0.612560215762571, 0.37552918835570065),\n",
       " (0.5825473149809889, 0.7081460229859158, 0.5297571033232632),\n",
       " (0.7256687831063818, 0.8088458309165923, 0.6922364699356445),\n",
       " (0.8615218829571156, 0.9044316381399372, 0.8464643849032072),\n",
       " (0.9377152439688033, 0.8667352942597744, 0.8865089633947292),\n",
       " (0.9121347085767019, 0.7115255968283856, 0.7697901858723735),\n",
       " (0.8851855717165659, 0.5480119207467544, 0.6468267583528324),\n",
       " (0.8596050363244643, 0.39280222331536563, 0.5301079808304767),\n",
       " (0.8340245009323628, 0.237592525883977, 0.413389203308121)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改各个参数，颜色随之变化\n",
    "# 修改h_neg,h_pos\n",
    "import seaborn as sns\n",
    "# 生成发散调色板\n",
    "palette = sns.diverging_palette(\n",
    "    h_neg=120,  # 设置左端颜色色相（0<=h_neg<=359）\n",
    "    h_pos=0, # 设置右端颜色色相（0<=h_pos<=359）\n",
    "    s=75,  # 设置颜色饱和度（0<=s<=100）\n",
    "    l=50,  # 设置颜色亮度（0<=l<=100）\n",
    "    sep=1,  # 介于h_neg h_pos之间区域大小\n",
    "    n=10,   # colormap颜色数量\n",
    "    center='light',  # 构建的colormap中间色调，light dark可选\n",
    "    as_cmap=False  # 返回列表而不是colormap对象\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "044130ea-b1eb-455e-96be-bb6ef4a5ebc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#4f8438;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#466f34;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3c5b2f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#32452a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#283025;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3f262c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#642b3b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8b314b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b0375a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d53d69;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.310841115279521, 0.516974408539226, 0.221301273388138),\n",
       " (0.2730284524365729, 0.4363368326159542, 0.202520920183905),\n",
       " (0.23521578959362485, 0.3556992566926824, 0.18374056697967203),\n",
       " (0.19538008598613008, 0.2707474354615736, 0.16395543343868912),\n",
       " (0.15756742314318198, 0.19010985953830173, 0.14517508023445616),\n",
       " (0.24733091920022782, 0.14848811309789303, 0.17335566150315485),\n",
       " (0.3920683903797272, 0.17047019672579994, 0.23257200359528876),\n",
       " (0.5445495585733641, 0.1936283586281631, 0.2949565191238532),\n",
       " (0.6892870297528635, 0.21561044225607007, 0.3541728612159871),\n",
       " (0.8340245009323628, 0.237592525883977, 0.413389203308121)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 修改center\n",
    "import seaborn as sns\n",
    "# 生成发散调色板\n",
    "palette = sns.diverging_palette(\n",
    "    h_neg=120,  # 设置左端颜色色相（0<=h_neg<=359）\n",
    "    h_pos=0, # 设置右端颜色色相（0<=h_pos<=359）\n",
    "    s=75,  # 设置颜色饱和度（0<=s<=100）\n",
    "    l=50,  # 设置颜色亮度（0<=l<=100）\n",
    "    sep=1,  # 介于h_neg h_pos之间区域大小\n",
    "    n=10,   # colormap颜色数量\n",
    "    center='dark',  # 构建的colormap中间色调，light dark可选\n",
    "    as_cmap=False  # 返回列表而不是colormap对象\n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c757b33f-20f0-4329-b9a3-0bff5f0f839b",
   "metadata": {},
   "source": [
    "其它颜色构建函数\n",
    "seaborn还有其它的一些颜色构建函数如blend_palette、crayon_palette等，使\r\n",
    "用方式都类似，感兴趣可以自行学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6a9de82f-e58f-40e6-a64d-c8965a1c5dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# color_palette\n",
    "'''\n",
    "color_palette可便利的结合前面的各类方法构建“sequential”，“diverging”，\n",
    "“qualitative”类colormap，功能部分和章节5.3.1.1～5.3.7中的方法重叠。\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "# 调用color_palette函数\n",
    "palette = sns.color_palette(\n",
    "    palette=None,  # 可以指定一个预定义的调色板名称，如deep，muted，pastel等\n",
    "    n_colors=None, # 指定颜色的数量，默认为当前matplotlib的颜色周期长度\n",
    "    desat=None,  # 指定每种颜色减少饱和度的比例，取值范围是0到1之间\n",
    "    as_cmap=False \n",
    ")\n",
    "palette"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e62e66-bdf7-4675-b897-e253faac7d16",
   "metadata": {},
   "source": [
    "color_palette中的palette参数\n",
    "palette参数支持多种类别的调色盘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e2d2ca-08ad-484c-beb0-fc57c5be4604",
   "metadata": {},
   "source": [
    "（一）seaborn调色盘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ada529c-714f-467b-a569-d937aebfb1fa",
   "metadata": {},
   "source": [
    "seaborn拥有6类修改版本的matplotlib palette（建议对照5.3.1.4章节的\n",
    "mpl_palette）按照亮度luminance和饱和度saturation变化区分：deep\r\n",
    "muted、bright、pastel、dark、colorblind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1dc90561-4ee5-47fd-9e3c-f83afa608373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#001c7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b1400d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#12711c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c0800;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#591e71;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#592f0d;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a23582;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#3c3c3c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b8850a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#006374;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.0, 0.10980392156862745, 0.4980392156862745),\n",
       " (0.6941176470588235, 0.25098039215686274, 0.050980392156862744),\n",
       " (0.07058823529411765, 0.44313725490196076, 0.10980392156862745),\n",
       " (0.5490196078431373, 0.03137254901960784, 0.0),\n",
       " (0.34901960784313724, 0.11764705882352941, 0.44313725490196076),\n",
       " (0.34901960784313724, 0.1843137254901961, 0.050980392156862744),\n",
       " (0.6352941176470588, 0.20784313725490197, 0.5098039215686274),\n",
       " (0.23529411764705882, 0.23529411764705882, 0.23529411764705882),\n",
       " (0.7215686274509804, 0.5215686274509804, 0.0392156862745098),\n",
       " (0.0, 0.38823529411764707, 0.4549019607843137)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.color_palette(palette='dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd39cb8a-88e3-4c1b-8b70-39e49bb5b68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a1c9f4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffb482;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8de5a1;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff9f9b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d0bbff;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#debb9b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fab0e4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cfcfcf;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fffea3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b9f2f0;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.6313725490196078, 0.788235294117647, 0.9568627450980393),\n",
       " (1.0, 0.7058823529411765, 0.5098039215686274),\n",
       " (0.5529411764705883, 0.8980392156862745, 0.6313725490196078),\n",
       " (1.0, 0.6235294117647059, 0.6078431372549019),\n",
       " (0.8156862745098039, 0.7333333333333333, 1.0),\n",
       " (0.8705882352941177, 0.7333333333333333, 0.6078431372549019),\n",
       " (0.9803921568627451, 0.6901960784313725, 0.8941176470588236),\n",
       " (0.8117647058823529, 0.8117647058823529, 0.8117647058823529),\n",
       " (1.0, 0.996078431372549, 0.6392156862745098),\n",
       " (0.7254901960784313, 0.9490196078431372, 0.9411764705882353)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='pastel')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daabad26-8061-4200-9e61-a257fbd6f1f7",
   "metadata": {},
   "source": [
    "（二） matplotlib调色盘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a2e9a4e6-135c-437d-9b46-3c68871322eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"440\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7fc97f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#beaed4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdc086;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#386cb0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f0027f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bf5b17;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#666666;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.4980392156862745, 0.788235294117647, 0.4980392156862745),\n",
       " (0.7450980392156863, 0.6823529411764706, 0.8313725490196079),\n",
       " (0.9921568627450981, 0.7529411764705882, 0.5254901960784314),\n",
       " (1.0, 1.0, 0.6),\n",
       " (0.2196078431372549, 0.4235294117647059, 0.6901960784313725),\n",
       " (0.9411764705882353, 0.00784313725490196, 0.4980392156862745),\n",
       " (0.7490196078431373, 0.3568627450980392, 0.09019607843137253),\n",
       " (0.4, 0.4, 0.4)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Accent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "690fbc58-b581-4b51-ab6b-17efe704d398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dbe9f6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bad6eb;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#89bedc;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#539ecd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2b7bba;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#0b559f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.8584083044982699, 0.9134486735870818, 0.9645674740484429),\n",
       " (0.7309496347558632, 0.8394771241830065, 0.9213225682429834),\n",
       " (0.5356862745098039, 0.746082276047674, 0.8642522106881968),\n",
       " (0.32628988850442137, 0.6186236063052672, 0.802798923490965),\n",
       " (0.16696655132641292, 0.48069204152249134, 0.7291503267973857),\n",
       " (0.044059976931949255, 0.3338869665513264, 0.6244521337946944)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e055dad9-9e04-4445-844c-a48928481979",
   "metadata": {},
   "source": [
    "直接使用matplotlib调色盘（参考章节：5.3.1.4 mpl_palette）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "87a549c3-baf5-45e0-bab8-e3ff8fcd1160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"660\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8dd3c7;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffffb3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bebada;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fb8072;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#80b1d3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdb462;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b3de69;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fccde5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d9d9d9;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bc80bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"550\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ccebc5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"605\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffed6f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.5529411764705883, 0.8274509803921568, 0.7803921568627451),\n",
       " (1.0, 1.0, 0.7019607843137254),\n",
       " (0.7450980392156863, 0.7294117647058823, 0.8549019607843137),\n",
       " (0.984313725490196, 0.5019607843137255, 0.4470588235294118),\n",
       " (0.5019607843137255, 0.6941176470588235, 0.8274509803921568),\n",
       " (0.9921568627450981, 0.7058823529411765, 0.3843137254901961),\n",
       " (0.7019607843137254, 0.8705882352941177, 0.4117647058823529),\n",
       " (0.9882352941176471, 0.803921568627451, 0.8980392156862745),\n",
       " (0.8509803921568627, 0.8509803921568627, 0.8509803921568627),\n",
       " (0.7372549019607844, 0.5019607843137255, 0.7411764705882353),\n",
       " (0.8, 0.9215686274509803, 0.7725490196078432),\n",
       " (1.0, 0.9294117647058824, 0.43529411764705883)]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Set3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb4950b-0fad-46b9-b894-7fee6503abea",
   "metadata": {},
   "source": [
    "（三）使用'husl' 或 'hls'调色盘"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80142d23-615c-4cd9-b186-cb5d8ff85f93",
   "metadata": {},
   "source": [
    "功能类似章节5.3.1.5 hls_palette，章节5.3.1.8 husl_palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c997bf37-91ec-44a9-8fac-0c874c7abd6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db5f57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dbae57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b9db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#69db57;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57db94;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#57d3db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5784db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c957db;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#db579e;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.86, 0.3712, 0.33999999999999997),\n",
       " (0.86, 0.6832, 0.33999999999999997),\n",
       " (0.7247999999999999, 0.86, 0.33999999999999997),\n",
       " (0.41279999999999994, 0.86, 0.33999999999999997),\n",
       " (0.33999999999999997, 0.86, 0.5792000000000002),\n",
       " (0.33999999999999997, 0.8287999999999999, 0.86),\n",
       " (0.33999999999999997, 0.5167999999999995, 0.86),\n",
       " (0.4752000000000003, 0.33999999999999997, 0.86),\n",
       " (0.7871999999999999, 0.33999999999999997, 0.86),\n",
       " (0.86, 0.33999999999999997, 0.6207999999999999)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='hls',n_colors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6af149ab-2c1b-4cf7-9e60-161f285b24a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f77189;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#dc8932;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ae9d31;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#77ab31;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#33b07a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#36ada4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#38a9c5;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6e9bf4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cc7af4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f565cc;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9677975592919913, 0.44127456009157356, 0.5358103155058701),\n",
       " (0.8616090647292522, 0.536495730113334, 0.19548899031476086),\n",
       " (0.6804189127793346, 0.6151497514677574, 0.19405452111445337),\n",
       " (0.46810256823426105, 0.6699492535792404, 0.1928958739904499),\n",
       " (0.20125317221201128, 0.6907920815379026, 0.47966761189275336),\n",
       " (0.21044753832183283, 0.6773105080456748, 0.6433941168468681),\n",
       " (0.2197995660828324, 0.6625157876850336, 0.7732093159317209),\n",
       " (0.433280341176423, 0.6065273407962815, 0.9585467098271748),\n",
       " (0.8004936186423958, 0.47703363533737203, 0.9579547196007522),\n",
       " (0.9622723935096688, 0.3976451968965351, 0.8008274363432775)]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='husl',n_colors=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e158108-625a-4935-b8d8-a4a26a28c6a4",
   "metadata": {},
   "source": [
    "（四）ch:<cubehelix 参数>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7020de-9da1-46c3-b26d-4ef29183feed",
   "metadata": {},
   "source": [
    "和章节5.3.1.1 cubehelix_palette部分功能重叠"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3603b98d-2131-4d8f-a2cc-1b8d0625e6c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c4e1e6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#98bed2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7597ba;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5b6f9c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#424773;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#26213f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.7698736219801169, 0.8824964869076034, 0.9031393192296783),\n",
       " (0.5958277847697188, 0.7466600001760163, 0.8229648794489529),\n",
       " (0.4607044852795941, 0.5932960229716936, 0.730649456041278),\n",
       " (0.35556204295569305, 0.43618710942955025, 0.6123052379682231),\n",
       " (0.25733291425658, 0.27690284066475196, 0.4511235980087324),\n",
       " (0.14873948341555188, 0.13056341212058453, 0.24877648177992634)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='ch:s=.15,rot=-.25',as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25862e3-1b5a-4d1c-bac7-d5427d18fa76",
   "metadata": {},
   "source": [
    "（五）'light:'、'dark:'、'blend:,"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59eacde4-57a6-4fce-83bf-06ddb264a055",
   "metadata": {},
   "source": [
    "功能类似章节5.3.1.2 dark_palette，章节5.3.1.3 dark_palette，及\n",
    "blend_palette方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "800f90a3-c00a-4524-b129-51e5f66d9968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f3f0f0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f5c0c0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f89090;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fa6060;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fd3030;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff0000;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.9522635075378764, 0.9411073728435433, 0.940983659029102),\n",
       " (0.9618108060303011, 0.7528858982748347, 0.7527869272232816),\n",
       " (0.9713581045227259, 0.564664423706126, 0.5645901954174611),\n",
       " (0.9809054030151505, 0.3764429491374174, 0.37639346361164083),\n",
       " (0.9904527015075753, 0.18822147456870864, 0.1881967318058203),\n",
       " (1.0, 0.0, 0.0)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='light:red',as_cmap=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6ba73161-1f56-413d-9090-8a9105194e1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#312222;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#5a1b1b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#831414;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ad0e0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d60707;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff0000;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.19218232522148992, 0.13308584448917626, 0.13306098410197983),\n",
       " (0.35374586017719195, 0.10646867559134102, 0.10644878728158387),\n",
       " (0.515309395132894, 0.07985150669350576, 0.0798365904611879),\n",
       " (0.676872930088596, 0.05323433779567051, 0.05322439364079194),\n",
       " (0.838436465044298, 0.026617168897835247, 0.026612196820395964),\n",
       " (1.0, 0.0, 0.0)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='dark:red',as_cmap=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2fa6904e-f2cb-483a-9a97-0c4c5be06b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#01a2d9;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#298bb6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#507493;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#785c6f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9f454c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c72e29;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.00392156862745098, 0.6352941176470588, 0.8509803921568627),\n",
       " (0.15921568627450983, 0.5443137254901961, 0.7129411764705882),\n",
       " (0.31450980392156863, 0.4533333333333333, 0.5749019607843138),\n",
       " (0.46980392156862744, 0.3623529411764706, 0.4368627450980392),\n",
       " (0.6250980392156863, 0.27137254901960783, 0.2988235294117647),\n",
       " (0.7803921568627451, 0.1803921568627451, 0.1607843137254902)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette=\"blend:#01a2d9,#c72e29\",as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126e0aa0-17cc-48e6-be60-00663140f629",
   "metadata": {},
   "source": [
    "（六）传入颜色序列作为调色盘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f90d264e-c369-4fbe-8638-cc10286cb845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"220\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#01a2d9;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#c72e29;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#31a354;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fb882c;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.00392156862745098, 0.6352941176470588, 0.8509803921568627),\n",
       " (0.7803921568627451, 0.1803921568627451, 0.1607843137254902),\n",
       " (0.19215686274509805, 0.6392156862745098, 0.32941176470588235),\n",
       " (0.984313725490196, 0.5333333333333333, 0.17254901960784313)]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette=['#01a2d9','#c72e29','#31A354','#FB882C'],as_cmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d944022-64d9-4baf-bf7c-e7f726b542c0",
   "metadata": {},
   "source": [
    "n_colors参数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69d28f4-99f4-4ee5-9b0a-56fec8b29784",
   "metadata": {},
   "source": [
    "当n_colors=None时，\n",
    "color_palette输出palette参数指定调色盘最大数量颜色（palette_n_colors），\r\n",
    "根据n_colors设置数量总共有以下情况"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da79b4ce-7adb-4a01-9164-27a9428b997f",
   "metadata": {},
   "source": [
    "n_colors=None，color_palette输出palette中所有颜色"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b599fd-9199-416b-a13e-54744ffbad7e",
   "metadata": {},
   "source": [
    "n_colors<palette_n_colors，color_palette输出palette中的n_colors种颜\n",
    "；"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b85bcf61-d45d-422b-999b-0c47c02ced80",
   "metadata": {},
   "source": [
    "n_colors>palette_n_colors，color_palette输出palette中的所有颜色\n",
    "+palette中的前（n_colors-palette_n_colors）种颜色，即此时会循环输\r\n",
    "palette中色；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9ecf3a82-2e4b-4124-ab56-5f1784fc8957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"440\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7fc97f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#beaed4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdc086;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#386cb0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f0027f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bf5b17;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#666666;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.4980392156862745, 0.788235294117647, 0.4980392156862745),\n",
       " (0.7450980392156863, 0.6823529411764706, 0.8313725490196079),\n",
       " (0.9921568627450981, 0.7529411764705882, 0.5254901960784314),\n",
       " (1.0, 1.0, 0.6),\n",
       " (0.2196078431372549, 0.4235294117647059, 0.6901960784313725),\n",
       " (0.9411764705882353, 0.00784313725490196, 0.4980392156862745),\n",
       " (0.7490196078431373, 0.3568627450980392, 0.09019607843137253),\n",
       " (0.4, 0.4, 0.4)]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Accent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a89213f8-3b08-43ad-83fe-95098cae7640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"330\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7fc97f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#beaed4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdc086;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#386cb0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f0027f;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.4980392156862745, 0.788235294117647, 0.4980392156862745),\n",
       " (0.7450980392156863, 0.6823529411764706, 0.8313725490196079),\n",
       " (0.9921568627450981, 0.7529411764705882, 0.5254901960784314),\n",
       " (1.0, 1.0, 0.6),\n",
       " (0.2196078431372549, 0.4235294117647059, 0.6901960784313725),\n",
       " (0.9411764705882353, 0.00784313725490196, 0.4980392156862745)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Accent',n_colors=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "022434a8-f9e7-4d35-ac8d-89679063fea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"605\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7fc97f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#beaed4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdc086;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#386cb0;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#f0027f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bf5b17;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#666666;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7fc97f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#beaed4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"550\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdc086;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.4980392156862745, 0.788235294117647, 0.4980392156862745),\n",
       " (0.7450980392156863, 0.6823529411764706, 0.8313725490196079),\n",
       " (0.9921568627450981, 0.7529411764705882, 0.5254901960784314),\n",
       " (1.0, 1.0, 0.6),\n",
       " (0.2196078431372549, 0.4235294117647059, 0.6901960784313725),\n",
       " (0.9411764705882353, 0.00784313725490196, 0.4980392156862745),\n",
       " (0.7490196078431373, 0.3568627450980392, 0.09019607843137253),\n",
       " (0.4, 0.4, 0.4),\n",
       " (0.4980392156862745, 0.788235294117647, 0.4980392156862745),\n",
       " (0.7450980392156863, 0.6823529411764706, 0.8313725490196079),\n",
       " (0.9921568627450981, 0.7529411764705882, 0.5254901960784314)]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(palette='Accent',n_colors=11)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
